Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "27" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 31 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 29 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459846 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 25.004090 | 25.827178 | 33.907271 | 34.969813 | 21.584638 | 20.987600 | 5.503814 | 4.284333 | 0.0322 | 0.0364 | 0.0043 | 1.180922 | 1.168729 |
| 2459845 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 16.962860 | 18.824379 | 39.725526 | 41.055111 | 10.152963 | 16.619516 | 2.930332 | 1.500911 | 0.0381 | 0.0439 | 0.0037 | 1.174519 | 1.225737 |
| 2459844 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 14.141155 | 16.278850 | 6.579364 | 6.701280 | 2.851572 | 3.036631 | 12.691107 | 8.547926 | 0.0244 | 0.0402 | 0.0031 | nan | nan |
| 2459843 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 17.540563 | 18.774093 | 19.300034 | 20.280510 | 64.466762 | 71.101993 | 2.947827 | 2.102283 | 0.0338 | 0.0401 | 0.0039 | 1.240236 | 1.238062 |
| 2459842 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 10.285105 | 12.913068 | 8.925461 | 9.910797 | -0.002358 | -0.556612 | 1.565102 | 1.186498 | 0.0319 | 0.0371 | 0.0042 | 1.330050 | 1.312261 |
| 2459841 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 14.176609 | 16.670217 | 4.615826 | 4.621565 | 4.917992 | 5.364263 | 10.198939 | 7.635722 | 0.0245 | 0.0359 | 0.0035 | nan | nan |
| 2459840 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 2.396144 | 2.605048 | 0.025032 | -0.797913 | 3.519174 | 2.654022 | 9.372518 | 4.795947 | 0.0235 | 0.0242 | 0.0007 | nan | nan |
| 2459839 | RF_maintenance | 100.00% | - | - | - | - | - | 0.198574 | 0.742902 | -0.154074 | -0.947548 | 1.736907 | 0.638160 | 11.884398 | 5.857192 | nan | nan | nan | nan | nan |
| 2459838 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 17.332220 | 20.202762 | 22.511192 | 23.562570 | 19.904382 | 29.840797 | 3.433787 | 2.599711 | 0.0400 | 0.0447 | 0.0034 | 1.334371 | 1.336256 |
| 2459836 | RF_maintenance | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0347 | 0.0356 | 0.0006 | nan | nan |
| 2459835 | RF_maintenance | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 2.255009 | 2.476446 | 2.327024 | 2.612209 | 0.597191 | 0.596385 | 1.563059 | 0.106871 | 0.0343 | 0.0357 | 0.0006 | nan | nan |
| 2459833 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 5.771018 | 5.942544 | 3.321429 | 3.222468 | 4.853202 | 3.662200 | 12.015070 | 8.876617 | 0.0376 | 0.0396 | 0.0020 | nan | nan |
| 2459832 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 31.983481 | 35.228902 | 25.165381 | 26.003809 | 13.836966 | 11.574374 | 3.489348 | 1.912456 | 0.0366 | 0.0399 | 0.0021 | 1.206198 | 1.216741 |
| 2459831 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | -0.348503 | 0.007238 | -0.302542 | -1.010644 | 1.488033 | 0.898940 | 7.974624 | 4.233333 | 0.0451 | 0.0483 | 0.0021 | nan | nan |
| 2459830 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 32.325830 | 34.343733 | 35.891200 | 36.981507 | 39.895369 | 35.942979 | 7.056712 | 4.775750 | 0.0370 | 0.0394 | 0.0022 | 1.315233 | 1.298765 |
| 2459829 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 29.717577 | 33.899539 | 29.288380 | 30.220037 | 28.785888 | 31.822004 | 9.946328 | 7.080164 | 0.0392 | 0.0432 | 0.0024 | 1.116852 | 1.112361 |
| 2459828 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 26.348217 | 28.808751 | 31.548592 | 32.333994 | 36.695343 | 33.153678 | 14.655764 | 11.852638 | 0.0388 | 0.0430 | 0.0029 | 1.280716 | 1.288200 |
| 2459827 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 23.422638 | 25.944644 | 35.551540 | 36.812062 | 24.645816 | 26.118563 | 1.862758 | 0.982565 | 0.0377 | 0.0412 | 0.0023 | 1.279685 | 1.282330 |
| 2459826 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 24.842043 | 26.194913 | 39.837316 | 40.659172 | 48.982357 | 45.024101 | 8.891619 | 7.124562 | 0.0390 | 0.0425 | 0.0023 | 1.339972 | 1.358363 |
| 2459825 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 27.657832 | 29.300466 | 31.717204 | 32.356343 | 27.707591 | 25.630320 | 1.634395 | 1.276432 | 0.0390 | 0.0436 | 0.0028 | 1.214378 | 1.192395 |
| 2459824 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 15.856355 | 19.879022 | 25.399366 | 26.286249 | 12.232212 | 20.017448 | 11.705316 | 10.428715 | 0.0357 | 0.0397 | 0.0024 | 1.132204 | 1.128737 |
| 2459823 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 24.674448 | 25.090014 | 47.289348 | 48.093592 | 34.417077 | 35.846334 | 31.159017 | 29.780164 | 0.0374 | 0.0408 | 0.0027 | 1.294258 | 1.306838 |
| 2459822 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 25.567127 | 26.643823 | 43.328030 | 44.257446 | 30.886411 | 29.495082 | 1.628701 | 0.828723 | 0.0396 | 0.0435 | 0.0023 | 1.226936 | 1.227375 |
| 2459821 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 26.929627 | 28.594501 | 44.468147 | 45.057191 | 27.900922 | 27.466158 | 1.384448 | 1.515821 | 0.0392 | 0.0431 | 0.0022 | 1.243437 | 1.251405 |
| 2459820 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 22.537327 | 25.310773 | 35.584683 | 36.804739 | 66.488348 | 68.781166 | 6.259777 | 5.203834 | 0.0422 | 0.0455 | 0.0022 | 1.228086 | 1.230825 |
| 2459817 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 23.375975 | 25.348799 | 43.114986 | 43.715518 | 36.435558 | 36.303545 | 3.815364 | 3.530491 | 0.0395 | 0.0441 | 0.0032 | 1.186933 | 1.193735 |
| 2459816 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 18.377470 | 20.581395 | 43.235765 | 44.489298 | 46.769307 | 45.276556 | 9.383095 | 7.384057 | 0.0398 | 0.0443 | 0.0029 | 1.244782 | 1.236372 |
| 2459815 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 22.860942 | 24.576314 | 47.088315 | 47.840239 | 47.677754 | 48.107964 | 12.413623 | 10.978550 | 0.0390 | 0.0444 | 0.0031 | 1.195399 | 1.204022 |
| 2459814 | RF_maintenance | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Power | 34.969813 | 25.004090 | 25.827178 | 33.907271 | 34.969813 | 21.584638 | 20.987600 | 5.503814 | 4.284333 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Power | 41.055111 | 18.824379 | 16.962860 | 41.055111 | 39.725526 | 16.619516 | 10.152963 | 1.500911 | 2.930332 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Shape | 16.278850 | 14.141155 | 16.278850 | 6.579364 | 6.701280 | 2.851572 | 3.036631 | 12.691107 | 8.547926 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Temporal Variability | 71.101993 | 18.774093 | 17.540563 | 20.280510 | 19.300034 | 71.101993 | 64.466762 | 2.102283 | 2.947827 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Shape | 12.913068 | 10.285105 | 12.913068 | 8.925461 | 9.910797 | -0.002358 | -0.556612 | 1.565102 | 1.186498 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Shape | 16.670217 | 14.176609 | 16.670217 | 4.615826 | 4.621565 | 4.917992 | 5.364263 | 10.198939 | 7.635722 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | ee Temporal Discontinuties | 9.372518 | 2.396144 | 2.605048 | 0.025032 | -0.797913 | 3.519174 | 2.654022 | 9.372518 | 4.795947 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | ee Temporal Discontinuties | 11.884398 | 0.742902 | 0.198574 | -0.947548 | -0.154074 | 0.638160 | 1.736907 | 5.857192 | 11.884398 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Temporal Variability | 29.840797 | 20.202762 | 17.332220 | 23.562570 | 22.511192 | 29.840797 | 19.904382 | 2.599711 | 3.433787 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Power | 2.612209 | 2.476446 | 2.255009 | 2.612209 | 2.327024 | 0.596385 | 0.597191 | 0.106871 | 1.563059 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | ee Temporal Discontinuties | 12.015070 | 5.942544 | 5.771018 | 3.222468 | 3.321429 | 3.662200 | 4.853202 | 8.876617 | 12.015070 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Shape | 35.228902 | 31.983481 | 35.228902 | 25.165381 | 26.003809 | 13.836966 | 11.574374 | 3.489348 | 1.912456 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | ee Temporal Discontinuties | 7.974624 | -0.348503 | 0.007238 | -0.302542 | -1.010644 | 1.488033 | 0.898940 | 7.974624 | 4.233333 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | ee Temporal Variability | 39.895369 | 32.325830 | 34.343733 | 35.891200 | 36.981507 | 39.895369 | 35.942979 | 7.056712 | 4.775750 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Shape | 33.899539 | 33.899539 | 29.717577 | 30.220037 | 29.288380 | 31.822004 | 28.785888 | 7.080164 | 9.946328 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | ee Temporal Variability | 36.695343 | 28.808751 | 26.348217 | 32.333994 | 31.548592 | 33.153678 | 36.695343 | 11.852638 | 14.655764 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Power | 36.812062 | 23.422638 | 25.944644 | 35.551540 | 36.812062 | 24.645816 | 26.118563 | 1.862758 | 0.982565 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | ee Temporal Variability | 48.982357 | 26.194913 | 24.842043 | 40.659172 | 39.837316 | 45.024101 | 48.982357 | 7.124562 | 8.891619 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Power | 32.356343 | 29.300466 | 27.657832 | 32.356343 | 31.717204 | 25.630320 | 27.707591 | 1.276432 | 1.634395 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Power | 26.286249 | 15.856355 | 19.879022 | 25.399366 | 26.286249 | 12.232212 | 20.017448 | 11.705316 | 10.428715 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Power | 48.093592 | 25.090014 | 24.674448 | 48.093592 | 47.289348 | 35.846334 | 34.417077 | 29.780164 | 31.159017 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Power | 44.257446 | 25.567127 | 26.643823 | 43.328030 | 44.257446 | 30.886411 | 29.495082 | 1.628701 | 0.828723 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Power | 45.057191 | 28.594501 | 26.929627 | 45.057191 | 44.468147 | 27.466158 | 27.900922 | 1.515821 | 1.384448 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Temporal Variability | 68.781166 | 22.537327 | 25.310773 | 35.584683 | 36.804739 | 66.488348 | 68.781166 | 6.259777 | 5.203834 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Power | 43.715518 | 23.375975 | 25.348799 | 43.114986 | 43.715518 | 36.435558 | 36.303545 | 3.815364 | 3.530491 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | ee Temporal Variability | 46.769307 | 20.581395 | 18.377470 | 44.489298 | 43.235765 | 45.276556 | 46.769307 | 7.384057 | 9.383095 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Temporal Variability | 48.107964 | 24.576314 | 22.860942 | 47.840239 | 47.088315 | 48.107964 | 47.677754 | 10.978550 | 12.413623 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | N01 | RF_maintenance | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |